The treatment of measurement error represents a common problem in many disciplines. The absence of an adequate model for such treatment can lead to data analyses with inaccurate results. This paper aims to provide a general overview of measurement error within confirmatory factor analysis, highlighting some of its theoretical foundations as well as the contexts in which it is modeled, and finally proposing a model, still under development, to address the issues that the inadequate representation of error can induce. The model in question distinguishes two different types of error, treating them through a mixture of covariances.
Il trattamento dell'errore di misurazione rappresenta un problema comune a molte discipline. L'assenza di un modello adeguato a tale trattamento può condurre ad analisi dei dati con risultati inaccurati. Il presente elaborato si propone di fornire una panoramica generale dell'errore di misurazione all'interno dell'analisi fattoriale confermativa, evidenziando alcune delle sue basi teoriche oltre ai contesti in cui viene modellato, proponendo infine un modello, ancora in via di sviluppo, per trattare le problematiche che l'inadeguata rappresentazione dell'errore può indurre. Il modello in questione distingue due diverse tipologie di errore trattandole tramite mistura di covarianze.
Separare la tipologia di errori in un modello CFA mediante mistura di covarianze d'errore
SCORTEGAGNA, MATTEO
2023/2024
Abstract
The treatment of measurement error represents a common problem in many disciplines. The absence of an adequate model for such treatment can lead to data analyses with inaccurate results. This paper aims to provide a general overview of measurement error within confirmatory factor analysis, highlighting some of its theoretical foundations as well as the contexts in which it is modeled, and finally proposing a model, still under development, to address the issues that the inadequate representation of error can induce. The model in question distinguishes two different types of error, treating them through a mixture of covariances.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/80140